Best Anomaly Detection Software for Azure Synapse Analytics
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Compare the Top Anomaly Detection Software that integrates with Azure Synapse Analytics as of December 2025
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This a list of Anomaly Detection software that integrates with Azure Synapse Analytics. Use the filters on the left to add additional filters for products that have integrations with Azure Synapse Analytics. View the products that work with Azure Synapse Analytics in the table below.
What is Anomaly Detection Software for Azure Synapse Analytics?
Anomaly detection software identifies unusual patterns, behaviors, or outliers in datasets that deviate from expected norms. It uses statistical, machine learning, and AI techniques to automatically detect anomalies in real time or through batch analysis. This software is widely used in cybersecurity, fraud detection, predictive maintenance, and quality control. By flagging anomalies, it enables early intervention, reduces risks, and enhances operational efficiency. Advanced versions offer customizable thresholds, real-time alerts, and integration with analytics dashboards for deeper insights. Compare and read user reviews of the best Anomaly Detection software for Azure Synapse Analytics currently available using the table below. This list is updated regularly.
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See how your data assets are used: popularity, utilization, and schema coverage. Get important insights about your data assets such as popularity, utilization, quality, and schema coverage. Find and filter the data you need based on metadata tags and descriptions. Get important insights about your data assets such as popularity, utilization, quality, and schema coverage. Drive data governance and ownership across your organization. Stream-lake-warehouse lineage to facilitate data ownership and collaboration. Automatically generated field-level lineage map to understand the entire data ecosystem. Anomaly detection learns from your data and seasonality patterns, with automatic backfill from historical data. Machine learning-based thresholds are trained per data segment, trained on actual data instead of metadata only.
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